From LLMs to AI Agents
Explore how large language models (LLMs) face limitations like outdated knowledge and context size, then learn how augmenting them with tools and dynamic context transforms them into autonomous AI agents. Understand the role of the Model Context Protocol in enabling scalable, adaptable AI systems that integrate real-time data, memory, and actions for task orchestration.
Imagine you have an extremely knowledgeable assistant who has read millions of books but is locked in a library with no phone, no internet, and only a notepad with one page. You ask this assistant a complex question about yesterday’s stock prices or to schedule a meeting on your calendar. The assistant wants to help, but it has two big problems:
It can only draw on its memory (which doesn’t include yesterday’s news or your schedule).
Its notepad is so small that it forgets anything that doesn’t fit on that page.
Limitations of LLMs
Large language models (LLMs) are powerful at generating and understanding text. However, when an LLM is used in isolation, it faces several inherent limitations:
Limited knowledge beyond training: LLMs only know what’s in their training data, with no awareness of recent events or new facts. They can’t access real-time information so that answers may be outdated or incorrect.
Pre-agent fix: Developers would periodically retrain or fine-tune models with newer data or manually inject facts into prompts to update knowledge.
Why not enough: Retraining is slow and costly; manual updates don’t scale. LLMs still can’t respond to new or user-specific info without extra help.
No built-in access to external data: By default, LLMs can’t browse the web or interact with other apps, as ...